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Coincidences of climate extremes and anomalous vegetation responses: Comparing tree ring patterns to simulated productivity
Climate extremes can trigger exceptional responses in terrestrial ecosystems, for instance by altering growth or mortality rates. Such effects are often manifested in reductions in net primary productivity (NPP). Investigating a Europe-wide network of annual radial tree growth records confirms this pattern: we find that 28% of tree ring width (TRW) indices are below two standard deviations in years in which extremely low precipitation, high temperatures or the combination of both noticeably affect tree growth. Based on these findings, we investigate possibilities for detecting climate-driven patterns in long-term TRW data to evaluate state-of-the-art dynamic vegetation models such as the Lund-Potsdam-Jena dynamic global vegetation model for managed land (LPJmL). The major problem in this context is that LPJmL simulates NPP but not explicitly the radial tree growth, and we need to develop a generic method to allow for a comparison between simulated and observed response patterns. We propose an analysis scheme that quantifies the coincidence rate of climate extremes with some biotic responses (here TRW or simulated NPP). We find a relative reduction of 34% in simulated NPP during precipitation, temperature and combined extremes. This reduction is comparable to the TRW response patterns, but the model responds much more sensitively to drought stress. We identify 10 extreme years during the 20th century during which both model and measurements indicate high coincidence rates across Europe. However, we detect substantial regional differences in simulated and observed responses to climatic extreme events. One explanation for this discrepancy could be the tendency of tree ring data to originate from climatically stressed sites. The difference between model and observed data is amplified by the fact that dynamic vegetation models are designed to simulate mean ecosystem responses on landscape or regional scales. We find that both simulation results and measurements display carry-over effects from climate anomalies during the previous year. We conclude that radial tree growth chronologies provide a suitable basis for generic model benchmarks. The broad application of coincidence analysis in generic model benchmarks along with an increased availability of representative long-term measurements and improved process-based models will refine projections of the long-term carbon balance in terrestrial ecosystems.
The world’s biggest gamble
The scale of the decarbonisation challenge to meet the Paris Agreement is underplayed in the public arena. It will require precipitous emissions reductions within 40 years and a new carbon sink on the scale of the ocean sink. Even then, the world is extremely likely to overshoot. A catastrophic failure of policy, for example, waiting another decade for transformative policy and full commitments to fossil‐free economies, will have irreversible and deleterious repercussions for humanity's remaining time on Earth. Only a global zero carbon roadmap will put the world on a course to phase‐out greenhouse gas emissions and create the essential carbon sinks for Earth‐system stability, without which, world prosperity is not possible.
Alternative carbon price trajectories can avoid excessive carbon removal
The large majority of climate change mitigation scenarios that hold warming below 2 °C show high deployment of carbon dioxide removal (CDR), resulting in a peak-and-decline behavior in global temperature. This is driven by the assumption of an exponentially increasing carbon price trajectory which is perceived to be economically optimal for meeting a carbon budget. However, this optimality relies on the assumption that a finite carbon budget associated with a temperature target is filled up steadily over time. The availability of net carbon removals invalidates this assumption and therefore a different carbon price trajectory should be chosen. We show how the optimal carbon price path for remaining well below 2 °C limits CDR demand and analyze requirements for constructing alternatives, which may be easier to implement in reality. We show that warming can be held at well below 2 °C at much lower long-term economic effort and lower CDR deployment and therefore lower risks if carbon prices are high enough in the beginning to ensure target compliance, but increase at a lower rate after carbon neutrality has been reached.
Measuring Success: Improving Assessments of Aggregate Greenhouse Gas Emissions Reduction Goals
Long-term success of the Paris Agreement will depend on the effectiveness of the instruments that it sets in place. Key among these are the nationally determined contributions (NDCs), which elaborate country-specific goals for mitigating and adapting to climate change. One role of the academic community and civil society in supporting the Paris Agreement is to assess the consistency between the near-term action under NDCs and the agreement's long-term goals, thereby providing insight into the chances of long-term success. Here we assess the strengths and weaknesses of current methods to estimate the effectiveness of the mitigation component of NDCs and identify the scientific and political advances that could be made to improve confidence in evaluating NDCs against the long-term goals. Specifically, we highlight (1) the influence of post-2030 assumptions on estimated 21st century warming, (2) uncertainties arising from the lack of published integrated assessment modeling scenarios with long-term, moderate effort reflecting a continuation of the current political situation, and (3) challenges in using a carbon budget approach. We further identify aspects that can be improved in the coming years: clearer communication regarding the meaning, likelihood, and timeframe of NDC consistent warming estimates; additional modeling of long-term, moderate action scenarios; and the identification of metrics for assessing progress that are not based solely on emissions, such as infrastructure investment, energy demand, or installed power capacity.
What metrics best reflect the energy and carbon intensity of cities? Insights from theory and modeling of 20 US cities
Three broad approaches have emerged for energy and greenhouse gas (GHG) accounting for individual cities: (a) purely in-boundary source-based accounting (IB); (b) community-wide infrastructure GHG emissions footprinting (CIF) incorporating life cycle GHGs (in-boundary plus trans-boundary) of key infrastructures providing water, energy, food, shelter, mobility-connectivity, waste management/sanitation and public amenities to support community-wide activities in cities - all resident, visitor, commercial and industrial activities; and (c) consumption-based GHG emissions footprints (CBF) incorporating life cycle GHGs associated with activities of a sub-set of the community - its final consumption sector dominated by resident households. The latter two activity-based accounts are recommended in recent GHG reporting standards, to provide production-dominated and consumption perspectives of cities, respectively. Little is known, however, on how to normalize and report the different GHG numbers that arise for the same city. We propose that CIF and IB, since they incorporate production, are best reported per unit GDP, while CBF is best reported per capita. Analysis of input-output models of 20 US cities shows that GHGCIF/GDP is well suited to represent differences in urban energy intensity features across cities, while GHGCBF/capita best represents variation in expenditures across cities. These results advance our understanding of the methods and metrics used to represent the energy and GHG performance of cities.
Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6
Grassland management affects the carbon fluxes of one-third of the global land area and is thus an important factor for the global carbon budget. Nonetheless, this aspect has been largely neglected or underrepresented in global carbon cycle models. We investigate four harvesting schemes for the managed grassland implementation of the dynamic global vegetation model (DGVM) Lund-Potsdam-Jena managed Land (LPJmL) that facilitate a better representation of actual management systems globally. We describe the model implementation and analyze simulation results with respect to harvest, net primary productivity and soil carbon content and by evaluating them against reported grass yields in Europe.We demonstrate the importance of accounting for differences in grassland management by assessing potential livestock grazing densities as well as the impacts of grazing, grazing intensities and mowing systems on soil carbon stocks. Grazing leads to soil carbon losses in polar or arid regions even at moderate livestock densities ( <0.4 livestock units per hectare-LSUha-1) but not in temperate regions even at much higher densities (0.4 to 1.2 LSUha-1). Applying LPJmL with the new grassland management options enables assessments of the global grassland production and its impact on the terrestrial biogeochemical cycles but requires a global data set on current grassland management.